Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-28T21:36:53.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating differences in brain activity between physical and digital prototyping in open and constrained design tasks

Published online by Cambridge University Press:  16 May 2024

Henrikke Dybvik ,
Adam McClenaghan ,
Mariya Stefanova Stoyanova Bond ,
Asbjørn Svergja ,
Tripp Shealy ,
Chris Snider ,
Pasi Aalto ,
Martin Steinert  and
Mark Goudswaard
Henrikke Dybvik*
Affiliation:
Norwegian University of Science and Technology, Norway University of Bristol, United Kingdom
Adam McClenaghan
Affiliation:
University of Bristol, United Kingdom
Mariya Stefanova Stoyanova Bond
Affiliation:
Norwegian University of Science and Technology, Norway
Asbjørn Svergja
Affiliation:
Norwegian University of Science and Technology, Norway
Tripp Shealy
Affiliation:
Virginia Tech, United States of America
Chris Snider
Affiliation:
University of Bristol, United Kingdom
Pasi Aalto
Affiliation:
Norwegian University of Science and Technology, Norway
Martin Steinert
Affiliation:
Norwegian University of Science and Technology, Norway
Mark Goudswaard
Affiliation:
University of Bristol, United Kingdom
*
[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This article presents an fNIRS experiment investigating cognitive differences between physical and digital prototyping methods in designers (N=25) engaged in open and constrained design tasks. Initial results suggest that physical prototyping yields increased hemodynamic response (i.e., brain activity) compared to digital design, and that constrained design yields increased hemodynamic response compared to open design, in the prefrontal cortex. Further work will seek to triangulate results by investigating potential correlations to design processes and design outputs.

Keywords

design cognitionprototypingvirtual prototypingcomputer-aided design (CAD)design activities
Type
Human Behaviour and Design Creativity
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 945 - 954
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Balters, S., Hawthorne, G., Reiss, A., 2023a. Priming Activity to Increase Interpersonal Closeness, Inter-brain Coherence, and Team Creativity Outcome.CrossRefGoogle Scholar
Balters, S., Miller, J.G., Li, R., Hawthorne, G., Reiss, A.L., 2023b. Virtual (Zoom) Interactions Alter Conversational Behavior and Interbrain Coherence. J. Neurosci. 43, 2568–2578. https://doi.org/10.1523/JNEUROSCI.1401-22.2023CrossRefGoogle ScholarPubMed
Barker, J.W., Rosso, A.L., Sparto, P.J., Huppert, T.J., 2016. Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy. NPh 3, 031410. https://doi.org/10.1117/1.NPh.3.3.031410Google ScholarPubMed
Benjamini, Y., Hochberg, Y., 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300.CrossRefGoogle Scholar
Donati, C., Vignoli, M., 2015. How tangible is your prototype? Designing the user and expert interaction. Int J Interact Des Manuf 9, 107–114. https://doi.org/10.1007/s12008-014-0232-5CrossRefGoogle Scholar
Ferrari, M., Quaresima, V., 2012. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage 63, 921–935. https://doi.org/10.1016/j.neuroimage.2012.03.049CrossRefGoogle ScholarPubMed
Fishburn, F.A., Ludlum, R.S., Vaidya, C.J., Medvedev, A.V., 2019. Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS. NeuroImage 184, 171–179. https://doi.org/10.1016/j.neuroimage.2018.09.025CrossRefGoogle ScholarPubMed
Goudswaard, M., Gopsill, J., Harvey, M., Snider, C., Bell, A., Hicks, B., 2021a. Revisiting Prototyping in 2020: A Snapshot of Practice in UK Design Companies. Proceedings of the Design Society 1, 2581–2590. https://doi.org/10.1017/pds.2021.519CrossRefGoogle Scholar
Goudswaard, M., Snider, C., Gopsill, J., Jones, D., Harvey, M., Hicks, B., 2021b. The prototyping fungibility framework. Procedia CIRP, 31st CIRP Design Conference 2021 (CIRP Design 2021) 100, 271–276. https://doi.org/10.1016/j.procir.2021.05.066CrossRefGoogle Scholar
Häggman, A., Tsai, G., Elsen, C., Honda, T., Yang, M.C., 2015. Connections Between the Design Tool, Design Attributes, and User Preferences in Early Stage Design. Journal of Mechanical Design 137. https://doi.org/10.1115/1.4030181CrossRefGoogle Scholar
Hart, S.G., 2006. NASA-task load index (NASA-TLX); 20 years later. Presented at the Proceedings of the human factors and ergonomics society annual meeting, Sage Publications Sage CA: Los Angeles, CA, pp. 904–908.CrossRefGoogle Scholar
Hernandez, S.M., Pollonini, L., 2020. NIRSplot: A Tool for Quality Assessment of fNIRS Scans, in: Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN). Presented at the Optics and the Brain, OSA, Washington, DC, p. BM2C.5. https://doi.org/10.1364/BRAIN.2020.BM2C.5CrossRefGoogle Scholar
Houde, S., Hill, C., 1997. What do prototypes prototype. Handbook of human-computer interaction 2, 367–381.CrossRefGoogle Scholar
Hu, M., Shealy, T., Milovanovic, J., 2021. Cognitive differences among first-year and senior engineering students when generating design solutions with and without additional dimensions of sustainability. Design Science 7, e1. https://doi.org/10.1017/dsj.2021.3CrossRefGoogle Scholar
Jacques, S.L., 2013. Optical properties of biological tissues: a review. Phys Med Biol 58, R37-61. https://doi.org/10.1088/0031-9155/58/11/R37CrossRefGoogle ScholarPubMed
Jensen, M.B., 2017. Opportunities of Industry-Based Makerspaces: New Ways of Prototyping in the Fuzzy Front End.Google Scholar
Kent, L., Snider, C., Gopsill, J., Hicks, B., 2021. Mixed reality in design prototyping: A systematic review. Design Studies 77, 101046. https://doi.org/10.1016/j.destud.2021.101046CrossRefGoogle Scholar
Leithner, C., Royl, G., 2014. The oxygen paradox of neurovascular coupling. J Cereb Blood Flow Metab 34, 19–29. https://doi.org/10.1038/jcbfm.2013.181CrossRefGoogle ScholarPubMed
LeoCad, 2022. LeoCAD - a CAD application for creating virtual lego models.Google Scholar
Lim, Y.-K., Stolterman, E., Tenenberg, J., 2008. The anatomy of prototypes: Prototypes as filters, prototypes as manifestations of design ideas. ACM Transactions on Computer-Human Interaction (TOCHI) 15, 7.CrossRefGoogle Scholar
Mathias, D., Hicks, B., Snider, C., Ranscombe, C., 2018. Characterising the Affordances and Limitations of Common Prototyping Techniques to Support The Early Stages of Product Development, in: DS 92: Proceedings of the DESIGN 2018 15th International Design Conference. Presented at the DESIGN 2018 - 15th International Design Conference, pp. 1257–1268. https://doi.org/10.21278/idc.2018.0445Google Scholar
McClenaghan, A., Goudswaard, M., Hicks, B., 2023. Investigating the Process, Design Outputs and Neurocognitive Differences between Prototyping Activities with Physical and Digital Lego, in: Proceedings of the International Conference on Engineering Design. Presented at the ICED23, Cambridge University Press, Bordeaux, France, pp. 2365–2374. https://doi.org/10.1017/pds.2023.237CrossRefGoogle Scholar
Montero-Hernandez, S., Pollonini, L., 2022. QT-NIRS (Quality Testing of Near Infrared Scans).Google Scholar
Oostenveld, R., Praamstra, P., 2001. The five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology 112, 713–719. https://doi.org/10.1016/S1388-2457(00)00527-7CrossRefGoogle ScholarPubMed
Peirce, J., Gray, J.R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., Lindeløv, J.K., 2019. PsychoPy2: Experiments in behavior made easy. Behav Res 51, 195–203. https://doi.org/10.3758/s13428-018-01193-yCrossRefGoogle ScholarPubMed
Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., Burgess, P.W., 2020. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann N Y Acad Sci 1464, 5–29. https://doi.org/10.1111/nyas.13948CrossRefGoogle ScholarPubMed
Russel, J.A., Weiss, A., Mendelsohn, G.A., 1989. Affect grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology 57, 493–502.CrossRefGoogle Scholar
Santosa, H., Zhai, X., Fishburn, F., Huppert, T., 2018. The NIRS Brain AnalyzIR Toolbox. Algorithms 11, 73. https://doi.org/10.3390/a11050073CrossRefGoogle ScholarPubMed
Shealy, T., Gero, J., Hu, M., Milovanovic, J., 2020. Concept generation techniques change patterns of brain activation during engineering design. Design Science 6. https://doi.org/10.1017/dsj.2020.30CrossRefGoogle Scholar
Vidulich, M.A., Tsang, P.S., 1987. Absolute Magnitude Estimation and Relative Judgement Approaches to Subjective Workload Assessment. Proceedings of the Human Factors Society Annual Meeting 31, 1057–1061. https://doi.org/10.1177/154193128703100930CrossRefGoogle Scholar
Vieira, S., Gero, J.S., Delmoral, J., Gattol, V., Fernandes, C., Parente, M., Fernandes, A.A., 2020a. The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving. Design Science 6. https://doi.org/10.1017/dsj.2020.26CrossRefGoogle Scholar
Vieira, S., Gero, J.S., Delmoral, J., Li, S., Cascini, G., Fernandes, A., 2020b. Brain activity in constrained and open design spaces: an EEG study, in: Proceedings of the Sixth International Conference on Design Creativity (ICDC 2020). pp. 068–075.Google Scholar
Yi, K., Heo, J., Hong, J., Kim, C., 2022. The role of the right prefrontal cortex in the retrieval of weak representations. Sci Rep 12, 4537. https://doi.org/10.1038/s41598-022-08493-6CrossRefGoogle ScholarPubMed
Morais, Zimeo, Balardin, G.A., Sato, J.B. J.R., 2018. fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Scientific Reports 8, 3341. https://doi.org/10.1038/s41598-018-21716-zCrossRefGoogle Scholar